Interested in this AI/ML Engineer role at Lumen?
Apply Now →About This Role
Lumen is the trusted network for the AI‑powered world, connecting people, data, and applications through our expansive fiber network and connected ecosystem. We enable secure, high‑performance connectivity across cloud, edge, and AI workloads for enterprises, governments, and communities.
At Lumen, you’ll work on infrastructure customers rely on today and build for what’s next, where performance, security, and resilience matter.
This is a high accountability environment where bold ideas drive real innovation for our customers, partners, and industry. The work is challenging, expectations are clear, and trust is built into how we operate. If you’re ready to take ownership, deliver meaningful impact, and help shape the future of AI‑ready connectivity, join us today.
The Role
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We are seeking an AI Operations Director, to serve as a strategic operator for the AI Office, helping translate Lumen’s AI ambition into clear priorities, operating mechanisms, executive alignment, and measurable enterprise impact. Reporting to the Chief AI Officer, this role will run the AI Office operating system – connecting strategy, portfolio discipline, governance forums, executive communications, stakeholder alignment, and follow\-through across the enterprise AI ecosystem. This person will help ensure the organization is focused on the right priorities, decisions are made with clarity and speed, and leaders have the visibility needed to accelerate value realization.
Working closely with AI strategy, technology, and business stakeholders, this person will bring structure to complexity, improve transparency across the portfolio, and accelerate value realization across the portfolio. They will also ensure the execution model is clear and effective so the organization can move with speed, focus, and alignment.
This is a high\-impact role connecting strategy to results through structure, governance, and operating cadence. We need a strategic thinker and versatile operator who can move fluidly between executive narrative, decision framing, portfolio orchestration and insight, operating cadence, stakeholder alignment, and execution discipline.
The Main Responsibilities
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- Partner closely with the Chief AI Officer to translate strategy into clear priorities, operating mechanisms, action plans, and executive narratives
- Serve as a strategic thought partner on where to invest, scale, pause, or stop across the AI portfolio
- Identify emerging gaps, risks, and opportunities across the AI ecosystem, and proactively drive the work needed to maintain clarity, momentum, and impact
- Support the AI Office as an enterprise leadership function, ensuring strategy, portfolio, governance, communications and execution stay connected
- Maintain a clear enterprise view of AI initiatives, including ownership, maturity, dependencies, sequencing considerations, execution risk, and value potential
- Ensure consistency in how initiatives are defined, tracked, and reported to enable clear, comparable visibility across the portfolio
- Identify and surface early indicators of fragmentation, duplication, or misalignment across initiatives
- Support prioritization decisions by synthesizing business, product, technical, and organizational signals into clear executive recommendations
- Design and manage the AI Office operating cadence, including portfolio reviews, executive updates, status reporting, and decision forums
- Stand up and manage portfolio governance, success metrics, and executive reporting to track performance, inform stage\-gate decisions, and measure business impact and AI value realization
- Enable faster, higher\-quality decisions by framing tradeoffs, clarifying implications, and driving alignment across stakeholders
- Act as a central connector across AI, Product, Engineering, HR, Legal, and business teams to ensure alignment and accountability
- Clarify ownership, sequencing, and interdependencies to improve execution
- Remove structural barriers that slow progress by identifying cross\-portfolio dependencies, decision bottlenecks, and areas of role confusion
- Build strong stakeholder relationships to drive accountability, adoption, and follow\-through
- Help define and track success metrics tied to business outcomes, adoption, portfolio health, and AI value realization
- Deliver clear, executive\-ready insights on progress, risks, themes, and impact
- Continuously improve and strengthen the AI Office operating model, including governance, planning, communication, and portfolio visibility
- Introduce scalable ways of working that improve focus, decision quality, and execution effectiveness
What We Look For in a Candidate
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- 8\+ years of experience in strategy \& operations, chief of staff, business ops, transformation, product/program management, or large\-scale transformation execution
- Strong strategic thinking and business judgment, with the ability to connect enterprise AI priorities to operating mechanisms, executive decisions, and measurable outcomes.
- Ability to move fluidly between strategic framing and hands\-on execution — from shaping executive narratives to driving follow\-through on complex cross\-functional work.
- Strong program management discipline, with experience creating structure, cadence, visibility, and accountability across complex initiatives.
- Proven experience managing complex, cross\-functional initiatives with multiple stakeholders
- Strong ability to translate strategy into executable plans and drive delivery at scale
- Experience working in or alongside data, AI, or technology organizations
- Exceptional stakeholder management, communication, and influence skills
- Demonstrated ability to operate in ambiguous, fast\-paced environments and drive outcomes
Compensation
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This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors.
Location Based Pay Ranges
$152,066 \- $253,444 in these states: AL AR AZ FL GA IA ID IN KS KY LA ME MO MS MT ND NE NM OH OK PA SC SD TN UT VT WI WV WY
$159,670 \- $266,166 in these states: CO HI MI MN NC NH NV OR RI
$167,273 \- $278,789 in these states: AK CA CT DC DE IL MA MD NJ NY TX VA WA
Lumen offers a comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing. We're able to answer any additional questions you may have about our bonus structure (short\-term incentives, long\-term incentives and/or sales compensation) as you move through the selection process.
Learn more about Lumen's:
- Benefits
- Bonus Structure
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Requisition \#: 342408
Life at Lumen
Life at Lumen is human and connected, even in a fast moving, AI‑focused organization. We set clear expectations and trust people to meet them. With real support and shared accountability, teams collaborate better, move faster, and deliver meaningful outcomes.
Our Lumen 8 behaviors guide how we interact, make decisions, and work together, shaping a culture built to perform and win.
To learn more about Life at Lumen and how we live the Lumen 8, please visit:
https://jobs.lumen.com/global/en/life\-at\-lumen
Background Screening
If you are selected for a position, there will be a background screen, which may include checks for criminal records and/or motor vehicle reports and/or drug screening, depending on the position requirements. For more information on these checks, please refer to the Post Offer section of our FAQ page. Job\-related concerns identified during the background screening may disqualify you from the new position or your current role. Background results will be evaluated on a case\-by\-case basis.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Equal Employment Opportunities
We are committed to providing equal employment opportunities to all persons regardless of race, color, ancestry, citizenship, national origin, religion, veteran status, disability, genetic characteristic or information, age, gender, sexual orientation, gender identity, gender expression, marital status, family status, pregnancy, or other legally protected status (collectively, “protected statuses”). We do not tolerate unlawful discrimination in any employment decisions, including recruiting, hiring, compensation, promotion, benefits, discipline, termination, job assignments or training.
Privacy Notice
Lumen is committed to protecting the privacy and security of personal information collected during the recruitment and hiring process. Our Privacy Notice explains how we collect, use, disclose, and protect applicant information, as well as how individuals may request access to or deletion of their personal data.
To review Lumen’s Privacy Notice, please visit:
https://jobs.lumen.com/global/en/privacy\-notice
Disclaimer
The job responsibilities described above indicate the general nature and level of work performed by employees within this classification. It is not intended to include a comprehensive inventory of all duties and responsibilities for this job. Job duties and responsibilities are subject to change based on evolving business needs and conditions.
In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Please be advised that Lumen does not require any form of payment from job applicants during the recruitment process. All legitimate job openings will be posted on our official website or communicated through official company email addresses. If you encounter any job offers that request payment in exchange for employment at Lumen, they are not for employment with us, but may relate to another company with a similar name.
Salary Context
This $152K-$278K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Lumen, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($215K) sits 20% above the category median. Disclosed range: $152K to $278K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Lumen AI Hiring
Lumen has 4 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $155K - $278K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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